This course provides an introduction to R and conveys fundamental skills of data literacy and the basics of data science.
It is suited for beginners and experienced students and contains 3 parts:

First, we introduce key concepts and commands of the R programming language for statistical computing. This includes working with the R Studio environment and writing reproducible research documents with R Markdown.

By working with different forms and types of data, the course provides a basic introduction to data literacy. Although later sessions add elements of computer programming (e.g., writing functions and loops), our focus remains on making sense of data (e.g., by creating summary tables and visualizations).

Regular exercises with real datasets explore the tools of the so-called tidyverse (including the R packages dplyr, ggplot, and tidyr).

The course is based on chapters of the popular R for data science textbook (Wickham & Grolemund, 2017), but topics and datasets used in the course are geared to the interests and needs of psychologists (e.g., involving the data of patients or experimental participants).
Completing this course enables students to understand, transform, analyze, and visualize data in a variety of ways. While this course does not deal with statistical testing and only scratches the surface of computer programming, it teaches reproducible research practices and covers fundamental knowledge and skills of data science.

Goals
Our main goal is to develop a set of useful skills in analyzing real-world data and conducting reproducible research. Upon completing this course, you will be able to read, transform, analyze, and visualize data of various types. Many interactive exercises will allow students to check their understanding, monitor their progress, and practice their skills.

Requirements
This course assumes some basic familiarity with statistics and the R programming language, but enthusiastic programming novices are also welcome.